人机协作提高监管写作的效率
Human-AI Collaboration Increases Efficiency in Regulatory Writing
AI总结：
本研究评估了大型语言模型平台AutoIND在监管写作中的效果，发现其能将新药临床试验申请初稿撰写时间减少约97%且无关键错误，但需专家进一步完善以达可提交质量。
摘要：
背景：新药临床试验（IND）申请准备耗时且依赖专业知识，减缓了早期临床开发。目的：评估大型语言模型（LLM）平台（AutoIND）能否在保持监管提交文件质量的同时缩短初稿撰写时间。方法：直接记录AutoIND生成的IND非临床书面摘要（eCTD模块2.6.2、2.6.4、2.6.6）的撰写时间。作为对比，根据监管撰写人员（≥6年经验）的经验，估算此前获美国FDA批准的IND摘要的人工撰写时间，并以此作为行业标准基准。质量由盲法监管写作评估员使用七个预先设定的类别进行评估：正确性、完整性、简洁性、一致性、清晰度、冗余性和重点。每个子标准评分为0-3分并归一化为百分比。关键监管错误定义为任何可能改变监管解读的错误陈述或遗漏（例如，错误的NOAEL，遗漏强制性GLP剂量配方分析）。结果：AutoIND将初稿撰写时间减少了约97%（对于IND-1中的18,870页/61份报告，从约100小时减少至3.7小时；对于IND-2中的11,425页/58份报告，减少至2.6小时）。IND-1和IND-2的质量得分分别为69.6%和77.9%。未检测到关键监管错误，但发现存在重点、简洁性和清晰度方面的不足。结论：AutoIND能显著加快IND撰写速度，但专家监管撰写人员对于将输出成熟至可提交质量仍然至关重要。已识别的系统性缺陷为针对性的模型改进提供了路线图。
英文摘要：
Background: Investigational New Drug (IND) application preparation is time-intensive and expertise-dependent, slowing early clinical development. Objective: To evaluate whether a large language model (LLM) platform (AutoIND) can reduce first-draft composition time while maintaining document quality in regulatory submissions. Methods: Drafting times for IND nonclinical written summaries (eCTD modules 2.6.2, 2.6.4, 2.6.6) generated by AutoIND were directly recorded. For comparison, manual drafting times for IND summaries previously cleared by the U.S. FDA were estimated from the experience of regulatory writers ($\geq$6 years) and used as industry-standard benchmarks. Quality was assessed by a blinded regulatory writing assessor using seven pre-specified categories: correctness, completeness, conciseness, consistency, clarity, redundancy, and emphasis. Each sub-criterion was scored 0-3 and normalized to a percentage. A critical regulatory error was defined as any misrepresentation or omission likely to alter regulatory interpretation (e.g., incorrect NOAEL, omission of mandatory GLP dose-formulation analysis). Results: AutoIND reduced initial drafting time by $\sim$97% (from $\sim$100 h to 3.7 h for 18,870 pages/61 reports in IND-1; and to 2.6 h for 11,425 pages/58 reports in IND-2). Quality scores were 69.6\% and 77.9\% for IND-1 and IND-2. No critical regulatory errors were detected, but deficiencies in emphasis, conciseness, and clarity were noted. Conclusions: AutoIND can dramatically accelerate IND drafting, but expert regulatory writers remain essential to mature outputs to submission-ready quality. Systematic deficiencies identified provide a roadmap for targeted model improvements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
